"AI-Driven Secure Vehicular Networks: Intelligent Threat Detection and Trust-Aware Defense for V2X Systems"

"Vehicular Ad Hoc Networks (VANETs) and Vehicle-to-Everything (V2X) systems are fundamental to intelligent transportation ecosystems. However, the highly dynamic topology, decentralized communication, and heterogeneous vehicle-edge infrastructure expose these networks to cyber threats such as Sybil attacks, spoofing, replay attacks, distributed denial-of-service, and malicious route manipulation. Traditional cryptographic defenses alone are insufficient against adaptive and zero-day threats.This paper proposes an AI-driven secure vehicular network framework that integrates federated learning, graph neural networks, and deep reinforcement learning for privacy-preserving intrusion detection, trust-aware routing, and autonomous attack mitigation. The architecture enables vehicles, roadside units, and edge servers to collaboratively train distributed models without sharing raw data, preserving privacy while improving detection generalization. A graph-based trust engine models inter-vehicle communication behavior, while DRL dynamically optimizes defense actions under changing network conditions. Experimental evaluation demonstrates superior performance in detection accuracy, false alarm reduction, attack response latency, and secure routing efficiency compared with conventional ML and signature-based IDS baselines"

车载自组织网络（Vehicular Ad Hoc Networks, VANETs）与车万物互联（Vehicle-to-Everything, V2X）系统是智能交通生态系统的核心基础设施。然而，高度动态的拓扑结构、去中心化的通信模式以及异构的车边基础设施，使得这类网络面临诸多网络安全威胁，包括女巫攻击（Sybil attack）、欺骗攻击、重放攻击、分布式拒绝服务攻击以及恶意路由篡改。仅依靠传统加密防御手段，无法应对自适应威胁与零日攻击。本文提出一种人工智能驱动的安全车载网络框架，该框架融合联邦学习（Federated Learning）、图神经网络（Graph Neural Network）与深度强化学习（Deep Reinforcement Learning, DRL），可实现隐私保护型入侵检测、信任感知路由与自主攻击缓解。该架构允许车辆、路边单元与边缘服务器在不共享原始数据的前提下协同训练分布式模型，在保护用户隐私的同时提升检测泛化能力。基于图结构的信任引擎可对车际通信行为进行建模，而深度强化学习（DRL）则可在网络环境动态变化时实时优化防御策略。实验评估结果表明，相较于传统机器学习方案与基于特征签名的入侵检测系统（Intrusion Detection System, IDS）基线，本框架在检测准确率、误报率降低、攻击响应延迟以及安全路由效率方面均展现出更优异的性能。